论文标题
对称对数符号机制的差异隐私
Differential privacy for symmetric log-concave mechanisms
论文作者
论文摘要
在数据库查询结果中添加随机噪声是实现隐私的重要工具。一个挑战是最大程度地减少这种噪音，同时仍然满足隐私要求。最近，发布了高斯噪声的$（ε，δ）$差分隐私的足够且必要的条件。此条件允许计算此分布的最小隐私量表。我们扩展了这项工作，并为$（ε，δ）$ - 差分隐私提供了足够且必要的条件，用于所有对称和对数孔噪声密度。我们的结果允许将噪声分布的细粒度调整为查询结果的维度。我们证明，与当前使用的拉普拉斯和高斯机制相同的$ε$和$δ$所产生的均方根误差可能会产生明显低的平方误差。
Adding random noise to database query results is an important tool for achieving privacy. A challenge is to minimize this noise while still meeting privacy requirements. Recently, a sufficient and necessary condition for $(ε, δ)$-differential privacy for Gaussian noise was published. This condition allows the computation of the minimum privacy-preserving scale for this distribution. We extend this work and provide a sufficient and necessary condition for $(ε, δ)$-differential privacy for all symmetric and log-concave noise densities. Our results allow fine-grained tailoring of the noise distribution to the dimensionality of the query result. We demonstrate that this can yield significantly lower mean squared errors than those incurred by the currently used Laplace and Gaussian mechanisms for the same $ε$ and $δ$.
